/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    LearningRateResultProducer.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.experiment;

import weka.core.AdditionalMeasureProducer;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;

import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

/**
 * <!-- globalinfo-start --> Tells a sub-ResultProducer to reproduce the current
 * run for varying sized subsamples of the dataset. Normally used with an
 * AveragingResultProducer and CrossValidationResultProducer combo to generate
 * learning curve results. For non-numeric result fields, the first value is
 * used.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -X &lt;num steps&gt;
 *  The number of steps in the learning rate curve.
 *  (default 10)
 * </pre>
 * 
 * <pre>
 * -W &lt;class name&gt;
 *  The full class name of a ResultProducer.
 *  eg: weka.experiment.CrossValidationResultProducer
 * </pre>
 * 
 * <pre>
 * Options specific to result producer weka.experiment.AveragingResultProducer:
 * </pre>
 * 
 * <pre>
 * -F &lt;field name&gt;
 *  The name of the field to average over.
 *  (default "Fold")
 * </pre>
 * 
 * <pre>
 * -X &lt;num results&gt;
 *  The number of results expected per average.
 *  (default 10)
 * </pre>
 * 
 * <pre>
 * -S
 *  Calculate standard deviations.
 *  (default only averages)
 * </pre>
 * 
 * <pre>
 * -W &lt;class name&gt;
 *  The full class name of a ResultProducer.
 *  eg: weka.experiment.CrossValidationResultProducer
 * </pre>
 * 
 * <pre>
 * Options specific to result producer weka.experiment.CrossValidationResultProducer:
 * </pre>
 * 
 * <pre>
 * -X &lt;number of folds&gt;
 *  The number of folds to use for the cross-validation.
 *  (default 10)
 * </pre>
 * 
 * <pre>
 * -D
 * Save raw split evaluator output.
 * </pre>
 * 
 * <pre>
 * -O &lt;file/directory name/path&gt;
 *  The filename where raw output will be stored.
 *  If a directory name is specified then then individual
 *  outputs will be gzipped, otherwise all output will be
 *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
 * </pre>
 * 
 * <pre>
 * -W &lt;class name&gt;
 *  The full class name of a SplitEvaluator.
 *  eg: weka.experiment.ClassifierSplitEvaluator
 * </pre>
 * 
 * <pre>
 * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
 * </pre>
 * 
 * <pre>
 * -W &lt;class name&gt;
 *  The full class name of the classifier.
 *  eg: weka.classifiers.bayes.NaiveBayes
 * </pre>
 * 
 * <pre>
 * -C &lt;index&gt;
 *  The index of the class for which IR statistics
 *  are to be output. (default 1)
 * </pre>
 * 
 * <pre>
 * -I &lt;index&gt;
 *  The index of an attribute to output in the
 *  results. This attribute should identify an
 *  instance in order to know which instances are
 *  in the test set of a cross validation. if 0
 *  no output (default 0).
 * </pre>
 * 
 * <pre>
 * -P
 *  Add target and prediction columns to the result
 *  for each fold.
 * </pre>
 * 
 * <pre>
 * Options specific to classifier weka.classifiers.rules.ZeroR:
 * </pre>
 * 
 * <pre>
 * -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * All options after -- will be passed to the result producer.
 * 
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @version $Revision: 6425 $
 */
public class LearningRateResultProducer implements ResultListener,
		ResultProducer, OptionHandler, AdditionalMeasureProducer,
		RevisionHandler {

	/** for serialization */
	static final long serialVersionUID = -3841159673490861331L;

	/** The dataset of interest */
	protected Instances m_Instances;

	/** The ResultListener to send results to */
	protected ResultListener m_ResultListener = new CSVResultListener();

	/** The ResultProducer used to generate results */
	protected ResultProducer m_ResultProducer = new AveragingResultProducer();

	/** The names of any additional measures to look for in SplitEvaluators */
	protected String[] m_AdditionalMeasures = null;

	/**
	 * The minimum number of instances to use. If this is zero, the first step
	 * will contain m_StepSize instances
	 */
	protected int m_LowerSize = 0;

	/**
	 * The maximum number of instances to use. -1 indicates no maximum (other
	 * than the total number of instances)
	 */
	protected int m_UpperSize = -1;

	/** The number of instances to add at each step */
	protected int m_StepSize = 10;

	/** The current dataset size during stepping */
	protected int m_CurrentSize = 0;

	/** The name of the key field containing the learning rate step number */
	public static String STEP_FIELD_NAME = "Total_instances";

	/**
	 * Returns a string describing this result producer
	 * 
	 * @return a description of the result producer suitable for displaying in
	 *         the explorer/experimenter gui
	 */
	public String globalInfo() {
		return "Tells a sub-ResultProducer to reproduce the current run for "
				+ "varying sized subsamples of the dataset. Normally used with "
				+ "an AveragingResultProducer and CrossValidationResultProducer "
				+ "combo to generate learning curve results. For non-numeric "
				+ "result fields, the first value is used.";
	}

	/**
	 * Determines if there are any constraints (imposed by the destination) on
	 * the result columns to be produced by resultProducers. Null should be
	 * returned if there are NO constraints, otherwise a list of column names
	 * should be returned as an array of Strings.
	 * 
	 * @param rp
	 *            the ResultProducer to which the constraints will apply
	 * @return an array of column names to which resutltProducer's results will
	 *         be restricted.
	 * @throws Exception
	 *             if constraints can't be determined
	 */
	public String[] determineColumnConstraints(ResultProducer rp)
			throws Exception {
		return null;
	}

	/**
	 * Gets the keys for a specified run number. Different run numbers
	 * correspond to different randomizations of the data. Keys produced should
	 * be sent to the current ResultListener
	 * 
	 * @param run
	 *            the run number to get keys for.
	 * @throws Exception
	 *             if a problem occurs while getting the keys
	 */
	public void doRunKeys(int run) throws Exception {

		if (m_ResultProducer == null) {
			throw new Exception("No ResultProducer set");
		}
		if (m_ResultListener == null) {
			throw new Exception("No ResultListener set");
		}
		if (m_Instances == null) {
			throw new Exception("No Instances set");
		}

		// Tell the resultproducer to send results to us
		m_ResultProducer.setResultListener(this);
		m_ResultProducer.setInstances(m_Instances);

		// For each subsample size
		if (m_LowerSize == 0) {
			m_CurrentSize = m_StepSize;
		} else {
			m_CurrentSize = m_LowerSize;
		}
		while (m_CurrentSize <= m_Instances.numInstances()
				&& ((m_UpperSize == -1) || (m_CurrentSize <= m_UpperSize))) {
			m_ResultProducer.doRunKeys(run);
			m_CurrentSize += m_StepSize;
		}
	}

	/**
	 * Gets the results for a specified run number. Different run numbers
	 * correspond to different randomizations of the data. Results produced
	 * should be sent to the current ResultListener
	 * 
	 * @param run
	 *            the run number to get results for.
	 * @throws Exception
	 *             if a problem occurs while getting the results
	 */
	public void doRun(int run) throws Exception {

		if (m_ResultProducer == null) {
			throw new Exception("No ResultProducer set");
		}
		if (m_ResultListener == null) {
			throw new Exception("No ResultListener set");
		}
		if (m_Instances == null) {
			throw new Exception("No Instances set");
		}

		// Randomize on a copy of the original dataset
		Instances runInstances = new Instances(m_Instances);
		runInstances.randomize(new Random(run));
		/*
		 * if (runInstances.classAttribute().isNominal()) {
		 * runInstances.stratify(m_StepSize); }
		 */

		// Tell the resultproducer to send results to us
		m_ResultProducer.setResultListener(this);

		// For each subsample size
		if (m_LowerSize == 0) {
			m_CurrentSize = m_StepSize;
		} else {
			m_CurrentSize = m_LowerSize;
		}
		while (m_CurrentSize <= m_Instances.numInstances()
				&& ((m_UpperSize == -1) || (m_CurrentSize <= m_UpperSize))) {
			m_ResultProducer.setInstances(new Instances(runInstances, 0,
					m_CurrentSize));
			m_ResultProducer.doRun(run);
			m_CurrentSize += m_StepSize;
		}
	}

	/**
	 * Prepare for the results to be received.
	 * 
	 * @param rp
	 *            the ResultProducer that will generate the results
	 * @throws Exception
	 *             if an error occurs during preprocessing.
	 */
	public void preProcess(ResultProducer rp) throws Exception {

		if (m_ResultListener == null) {
			throw new Exception("No ResultListener set");
		}
		m_ResultListener.preProcess(this);
	}

	/**
	 * Prepare to generate results. The ResultProducer should call
	 * preProcess(this) on the ResultListener it is to send results to.
	 * 
	 * @throws Exception
	 *             if an error occurs during preprocessing.
	 */
	public void preProcess() throws Exception {

		if (m_ResultProducer == null) {
			throw new Exception("No ResultProducer set");
		}
		// Tell the resultproducer to send results to us
		m_ResultProducer.setResultListener(this);
		m_ResultProducer.preProcess();
	}

	/**
	 * When this method is called, it indicates that no more results will be
	 * sent that need to be grouped together in any way.
	 * 
	 * @param rp
	 *            the ResultProducer that generated the results
	 * @throws Exception
	 *             if an error occurs
	 */
	public void postProcess(ResultProducer rp) throws Exception {

		m_ResultListener.postProcess(this);
	}

	/**
	 * When this method is called, it indicates that no more requests to
	 * generate results for the current experiment will be sent. The
	 * ResultProducer should call preProcess(this) on the ResultListener it is
	 * to send results to.
	 * 
	 * @throws Exception
	 *             if an error occurs
	 */
	public void postProcess() throws Exception {

		m_ResultProducer.postProcess();
	}

	/**
	 * Accepts results from a ResultProducer.
	 * 
	 * @param rp
	 *            the ResultProducer that generated the results
	 * @param key
	 *            an array of Objects (Strings or Doubles) that uniquely
	 *            identify a result for a given ResultProducer with given
	 *            compatibilityState
	 * @param result
	 *            the results stored in an array. The objects stored in the
	 *            array may be Strings, Doubles, or null (for the missing
	 *            value).
	 * @throws Exception
	 *             if the result could not be accepted.
	 */
	public void acceptResult(ResultProducer rp, Object[] key, Object[] result)
			throws Exception {

		if (m_ResultProducer != rp) {
			throw new Error("Unrecognized ResultProducer sending results!!");
		}
		// Add in current step as key field
		Object[] newKey = new Object[key.length + 1];
		System.arraycopy(key, 0, newKey, 0, key.length);
		newKey[key.length] = new String("" + m_CurrentSize);
		// Pass on to result listener
		m_ResultListener.acceptResult(this, newKey, result);
	}

	/**
	 * Determines whether the results for a specified key must be generated.
	 * 
	 * @param rp
	 *            the ResultProducer wanting to generate the results
	 * @param key
	 *            an array of Objects (Strings or Doubles) that uniquely
	 *            identify a result for a given ResultProducer with given
	 *            compatibilityState
	 * @return true if the result should be generated
	 * @throws Exception
	 *             if it could not be determined if the result is needed.
	 */
	public boolean isResultRequired(ResultProducer rp, Object[] key)
			throws Exception {

		if (m_ResultProducer != rp) {
			throw new Error("Unrecognized ResultProducer sending results!!");
		}
		// Add in current step as key field
		Object[] newKey = new Object[key.length + 1];
		System.arraycopy(key, 0, newKey, 0, key.length);
		newKey[key.length] = new String("" + m_CurrentSize);
		// Pass on request to result listener
		return m_ResultListener.isResultRequired(this, newKey);
	}

	/**
	 * Gets the names of each of the columns produced for a single run.
	 * 
	 * @return an array containing the name of each column
	 * @throws Exception
	 *             if key names cannot be generated
	 */
	public String[] getKeyNames() throws Exception {

		String[] keyNames = m_ResultProducer.getKeyNames();
		String[] newKeyNames = new String[keyNames.length + 1];
		System.arraycopy(keyNames, 0, newKeyNames, 0, keyNames.length);
		// Think of a better name for this key field
		newKeyNames[keyNames.length] = STEP_FIELD_NAME;
		return newKeyNames;
	}

	/**
	 * Gets the data types of each of the columns produced for a single run.
	 * This method should really be static.
	 * 
	 * @return an array containing objects of the type of each column. The
	 *         objects should be Strings, or Doubles.
	 * @throws Exception
	 *             if the key types could not be determined (perhaps because of
	 *             a problem from a nested sub-resultproducer)
	 */
	public Object[] getKeyTypes() throws Exception {

		Object[] keyTypes = m_ResultProducer.getKeyTypes();
		Object[] newKeyTypes = new Object[keyTypes.length + 1];
		System.arraycopy(keyTypes, 0, newKeyTypes, 0, keyTypes.length);
		newKeyTypes[keyTypes.length] = "";
		return newKeyTypes;
	}

	/**
	 * Gets the names of each of the columns produced for a single run. A new
	 * result field is added for the number of results used to produce each
	 * average. If only averages are being produced the names are not altered,
	 * if standard deviations are produced then "Dev_" and "Avg_" are prepended
	 * to each result deviation and average field respectively.
	 * 
	 * @return an array containing the name of each column
	 * @throws Exception
	 *             if the result names could not be determined (perhaps because
	 *             of a problem from a nested sub-resultproducer)
	 */
	public String[] getResultNames() throws Exception {

		return m_ResultProducer.getResultNames();
	}

	/**
	 * Gets the data types of each of the columns produced for a single run.
	 * 
	 * @return an array containing objects of the type of each column. The
	 *         objects should be Strings, or Doubles.
	 * @throws Exception
	 *             if the result types could not be determined (perhaps because
	 *             of a problem from a nested sub-resultproducer)
	 */
	public Object[] getResultTypes() throws Exception {

		return m_ResultProducer.getResultTypes();
	}

	/**
	 * Gets a description of the internal settings of the result producer,
	 * sufficient for distinguishing a ResultProducer instance from another with
	 * different settings (ignoring those settings set through this interface).
	 * For example, a cross-validation ResultProducer may have a setting for the
	 * number of folds. For a given state, the results produced should be
	 * compatible. Typically if a ResultProducer is an OptionHandler, this
	 * string will represent the command line arguments required to set the
	 * ResultProducer to that state.
	 * 
	 * @return the description of the ResultProducer state, or null if no state
	 *         is defined
	 */
	public String getCompatibilityState() {

		String result = " ";
		// + "-F " + Utils.quote(getKeyFieldName())
		// + " -X " + getStepSize() + " ";
		if (m_ResultProducer == null) {
			result += "<null ResultProducer>";
		} else {
			result += "-W " + m_ResultProducer.getClass().getName();
			result += " -- " + m_ResultProducer.getCompatibilityState();
		}

		return result.trim();
	}

	/**
	 * Returns an enumeration describing the available options..
	 * 
	 * @return an enumeration of all the available options.
	 */
	public Enumeration listOptions() {

		Vector newVector = new Vector(2);

		newVector.addElement(new Option(
				"\tThe number of steps in the learning rate curve.\n"
						+ "\t(default 10)", "X", 1, "-X <num steps>"));
		newVector
				.addElement(new Option(
						"\tThe full class name of a ResultProducer.\n"
								+ "\teg: weka.experiment.CrossValidationResultProducer",
						"W", 1, "-W <class name>"));

		if ((m_ResultProducer != null)
				&& (m_ResultProducer instanceof OptionHandler)) {
			newVector.addElement(new Option("", "", 0,
					"\nOptions specific to result producer "
							+ m_ResultProducer.getClass().getName() + ":"));
			Enumeration enu = ((OptionHandler) m_ResultProducer).listOptions();
			while (enu.hasMoreElements()) {
				newVector.addElement(enu.nextElement());
			}
		}
		return newVector.elements();
	}

	/**
	 * Parses a given list of options.
	 * <p/>
	 * 
	 * <!-- options-start --> Valid options are:
	 * <p/>
	 * 
	 * <pre>
	 * -X &lt;num steps&gt;
	 *  The number of steps in the learning rate curve.
	 *  (default 10)
	 * </pre>
	 * 
	 * <pre>
	 * -W &lt;class name&gt;
	 *  The full class name of a ResultProducer.
	 *  eg: weka.experiment.CrossValidationResultProducer
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to result producer weka.experiment.AveragingResultProducer:
	 * </pre>
	 * 
	 * <pre>
	 * -F &lt;field name&gt;
	 *  The name of the field to average over.
	 *  (default "Fold")
	 * </pre>
	 * 
	 * <pre>
	 * -X &lt;num results&gt;
	 *  The number of results expected per average.
	 *  (default 10)
	 * </pre>
	 * 
	 * <pre>
	 * -S
	 *  Calculate standard deviations.
	 *  (default only averages)
	 * </pre>
	 * 
	 * <pre>
	 * -W &lt;class name&gt;
	 *  The full class name of a ResultProducer.
	 *  eg: weka.experiment.CrossValidationResultProducer
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to result producer weka.experiment.CrossValidationResultProducer:
	 * </pre>
	 * 
	 * <pre>
	 * -X &lt;number of folds&gt;
	 *  The number of folds to use for the cross-validation.
	 *  (default 10)
	 * </pre>
	 * 
	 * <pre>
	 * -D
	 * Save raw split evaluator output.
	 * </pre>
	 * 
	 * <pre>
	 * -O &lt;file/directory name/path&gt;
	 *  The filename where raw output will be stored.
	 *  If a directory name is specified then then individual
	 *  outputs will be gzipped, otherwise all output will be
	 *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
	 * </pre>
	 * 
	 * <pre>
	 * -W &lt;class name&gt;
	 *  The full class name of a SplitEvaluator.
	 *  eg: weka.experiment.ClassifierSplitEvaluator
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
	 * </pre>
	 * 
	 * <pre>
	 * -W &lt;class name&gt;
	 *  The full class name of the classifier.
	 *  eg: weka.classifiers.bayes.NaiveBayes
	 * </pre>
	 * 
	 * <pre>
	 * -C &lt;index&gt;
	 *  The index of the class for which IR statistics
	 *  are to be output. (default 1)
	 * </pre>
	 * 
	 * <pre>
	 * -I &lt;index&gt;
	 *  The index of an attribute to output in the
	 *  results. This attribute should identify an
	 *  instance in order to know which instances are
	 *  in the test set of a cross validation. if 0
	 *  no output (default 0).
	 * </pre>
	 * 
	 * <pre>
	 * -P
	 *  Add target and prediction columns to the result
	 *  for each fold.
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to classifier weka.classifiers.rules.ZeroR:
	 * </pre>
	 * 
	 * <pre>
	 * -D
	 *  If set, classifier is run in debug mode and
	 *  may output additional info to the console
	 * </pre>
	 * 
	 * <!-- options-end -->
	 * 
	 * All options after -- will be passed to the result producer.
	 * 
	 * @param options
	 *            the list of options as an array of strings
	 * @throws Exception
	 *             if an option is not supported
	 */
	public void setOptions(String[] options) throws Exception {

		String stepSize = Utils.getOption('S', options);
		if (stepSize.length() != 0) {
			setStepSize(Integer.parseInt(stepSize));
		} else {
			setStepSize(10);
		}

		String lowerSize = Utils.getOption('L', options);
		if (lowerSize.length() != 0) {
			setLowerSize(Integer.parseInt(lowerSize));
		} else {
			setLowerSize(0);
		}

		String upperSize = Utils.getOption('U', options);
		if (upperSize.length() != 0) {
			setUpperSize(Integer.parseInt(upperSize));
		} else {
			setUpperSize(-1);
		}

		String rpName = Utils.getOption('W', options);
		if (rpName.length() == 0) {
			throw new Exception("A ResultProducer must be specified with"
					+ " the -W option.");
		}
		// Do it first without options, so if an exception is thrown during
		// the option setting, listOptions will contain options for the actual
		// RP.
		setResultProducer((ResultProducer) Utils.forName(ResultProducer.class,
				rpName, null));
		if (getResultProducer() instanceof OptionHandler) {
			((OptionHandler) getResultProducer()).setOptions(Utils
					.partitionOptions(options));
		}
	}

	/**
	 * Gets the current settings of the result producer.
	 * 
	 * @return an array of strings suitable for passing to setOptions
	 */
	public String[] getOptions() {

		String[] seOptions = new String[0];
		if ((m_ResultProducer != null)
				&& (m_ResultProducer instanceof OptionHandler)) {
			seOptions = ((OptionHandler) m_ResultProducer).getOptions();
		}

		String[] options = new String[seOptions.length + 9];
		int current = 0;

		options[current++] = "-S";
		options[current++] = "" + getStepSize();
		options[current++] = "-L";
		options[current++] = "" + getLowerSize();
		options[current++] = "-U";
		options[current++] = "" + getUpperSize();
		if (getResultProducer() != null) {
			options[current++] = "-W";
			options[current++] = getResultProducer().getClass().getName();
		}
		options[current++] = "--";

		System.arraycopy(seOptions, 0, options, current, seOptions.length);
		current += seOptions.length;
		while (current < options.length) {
			options[current++] = "";
		}
		return options;
	}

	/**
	 * Set a list of method names for additional measures to look for in
	 * SplitEvaluators. This could contain many measures (of which only a subset
	 * may be produceable by the current resultProducer) if an experiment is the
	 * type that iterates over a set of properties.
	 * 
	 * @param additionalMeasures
	 *            an array of measure names, null if none
	 */
	public void setAdditionalMeasures(String[] additionalMeasures) {
		m_AdditionalMeasures = additionalMeasures;

		if (m_ResultProducer != null) {
			System.err
					.println("LearningRateResultProducer: setting additional "
							+ "measures for " + "ResultProducer");
			m_ResultProducer.setAdditionalMeasures(m_AdditionalMeasures);
		}
	}

	/**
	 * Returns an enumeration of any additional measure names that might be in
	 * the result producer
	 * 
	 * @return an enumeration of the measure names
	 */
	public Enumeration enumerateMeasures() {
		Vector newVector = new Vector();
		if (m_ResultProducer instanceof AdditionalMeasureProducer) {
			Enumeration en = ((AdditionalMeasureProducer) m_ResultProducer)
					.enumerateMeasures();
			while (en.hasMoreElements()) {
				String mname = (String) en.nextElement();
				newVector.addElement(mname);
			}
		}
		return newVector.elements();
	}

	/**
	 * Returns the value of the named measure
	 * 
	 * @param additionalMeasureName
	 *            the name of the measure to query for its value
	 * @return the value of the named measure
	 * @throws IllegalArgumentException
	 *             if the named measure is not supported
	 */
	public double getMeasure(String additionalMeasureName) {
		if (m_ResultProducer instanceof AdditionalMeasureProducer) {
			return ((AdditionalMeasureProducer) m_ResultProducer)
					.getMeasure(additionalMeasureName);
		} else {
			throw new IllegalArgumentException("LearningRateResultProducer: "
					+ "Can't return value for : " + additionalMeasureName
					+ ". " + m_ResultProducer.getClass().getName() + " "
					+ "is not an AdditionalMeasureProducer");
		}
	}

	/**
	 * Sets the dataset that results will be obtained for.
	 * 
	 * @param instances
	 *            a value of type 'Instances'.
	 */
	public void setInstances(Instances instances) {

		m_Instances = instances;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String lowerSizeTipText() {
		return "Set the minmum number of instances in a dataset. Setting zero "
				+ "here will actually use <stepSize> number of instances at the first "
				+ "step (since it makes no sense to use zero instances :-))";
	}

	/**
	 * Get the value of LowerSize.
	 * 
	 * @return Value of LowerSize.
	 */
	public int getLowerSize() {

		return m_LowerSize;
	}

	/**
	 * Set the value of LowerSize.
	 * 
	 * @param newLowerSize
	 *            Value to assign to LowerSize.
	 */
	public void setLowerSize(int newLowerSize) {

		m_LowerSize = newLowerSize;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String upperSizeTipText() {
		return "Set the maximum number of instances in a dataset. Setting -1 "
				+ "sets no upper limit (other than the total number of instances "
				+ "in the full dataset)";
	}

	/**
	 * Get the value of UpperSize.
	 * 
	 * @return Value of UpperSize.
	 */
	public int getUpperSize() {

		return m_UpperSize;
	}

	/**
	 * Set the value of UpperSize.
	 * 
	 * @param newUpperSize
	 *            Value to assign to UpperSize.
	 */
	public void setUpperSize(int newUpperSize) {

		m_UpperSize = newUpperSize;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String stepSizeTipText() {
		return "Set the number of instances to add at each step.";
	}

	/**
	 * Get the value of StepSize.
	 * 
	 * @return Value of StepSize.
	 */
	public int getStepSize() {

		return m_StepSize;
	}

	/**
	 * Set the value of StepSize.
	 * 
	 * @param newStepSize
	 *            Value to assign to StepSize.
	 */
	public void setStepSize(int newStepSize) {

		m_StepSize = newStepSize;
	}

	/**
	 * Sets the object to send results of each run to.
	 * 
	 * @param listener
	 *            a value of type 'ResultListener'
	 */
	public void setResultListener(ResultListener listener) {

		m_ResultListener = listener;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String resultProducerTipText() {
		return "Set the resultProducer for which learning rate results should be "
				+ "generated.";
	}

	/**
	 * Get the ResultProducer.
	 * 
	 * @return the ResultProducer.
	 */
	public ResultProducer getResultProducer() {

		return m_ResultProducer;
	}

	/**
	 * Set the ResultProducer.
	 * 
	 * @param newResultProducer
	 *            new ResultProducer to use.
	 */
	public void setResultProducer(ResultProducer newResultProducer) {

		m_ResultProducer = newResultProducer;
		m_ResultProducer.setResultListener(this);
	}

	/**
	 * Gets a text descrption of the result producer.
	 * 
	 * @return a text description of the result producer.
	 */
	public String toString() {

		String result = "LearningRateResultProducer: ";
		result += getCompatibilityState();
		if (m_Instances == null) {
			result += ": <null Instances>";
		} else {
			result += ": " + Utils.backQuoteChars(m_Instances.relationName());
		}
		return result;
	}

	/**
	 * Returns the revision string.
	 * 
	 * @return the revision
	 */
	public String getRevision() {
		return RevisionUtils.extract("$Revision: 6425 $");
	}
} // LearningRateResultProducer
